Planning in entropy-regularized Markov decision processes and games
Abstract
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1/epsilon^4) for a desired accuracy epsilon, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.
Fields of Study & Subjects
Reinforcement Learning in Robotics
Artificial Intelligence in Games
Advanced Bandit Algorithms Research
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 21 April 2026
- Language
- EN
- Document type
- Preprint